Afatinib in Advanced Pretreated Non-Small-Cell Lung Cancer—A Canadian Experience
Bibliographic record
Abstract
Background: Afatinib, an irreversible epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI), is approved for first-line therapy in advanced EGFR mutation–positive non-small-cell lung cancer (NSCLC) and has previously demonstrated activity after failure of chemotherapy and reversible EGFR TKI, with improved response and progression-free survival, compared with placebo. Outcomes in pretreated patients with advanced NSCLC receiving afatinib through a Canadian special access program (sap) are reported here. Methods: Patients with NSCLC progressing after at least 1 line of chemotherapy and an EGFR TKI were eligible to enrol in the sap. Characteristics of patients from the two largest accruing Canadian centres were retrospectively reviewed, including demographics, disease and treatment data, and patient outcomes. Results: The 53 patients who received afatinib (57% women, 51% never-smokers, 26% of East Asian ethnicity, and 66% with adenocarcinoma) had a median age of 59 years. EGFR mutations were documented in 25%, and EGFR wildtype in 8%. All patients had received prior EGFR TKI treatment, with 42% achieving a response. Patients took afatinib for a median of 2 months (range: 0–26 months); 17% required 1 or more dose reductions. Of 47 evaluable patients receiving afatinib, 10 experienced tumour shrinkage, and 11, stable disease. Median survival from afatinib initiation was 5 months (95% confidence interval: 2 months to 8 months). Grade 3 or greater diarrhea, rash, paronychia, and stomatitis were seen in 9%, 11%, 6%, and 4% of patients respectively. Conclusions: In an unselected population of pretreated patients with advanced NSCLC after TKI failure, median survival with afatinib therapy was 5 months. Through a sap, afatinib demonstrated activity in clinical practice, with manageable toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".